CN111751732B - Electric quantity calculation method based on self-adaptive Gaussian convolution integral method - Google Patents
Electric quantity calculation method based on self-adaptive Gaussian convolution integral method Download PDFInfo
- Publication number
- CN111751732B CN111751732B CN202010761027.8A CN202010761027A CN111751732B CN 111751732 B CN111751732 B CN 111751732B CN 202010761027 A CN202010761027 A CN 202010761027A CN 111751732 B CN111751732 B CN 111751732B
- Authority
- CN
- China
- Prior art keywords
- capacity
- value
- moment
- battery
- temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The invention relates to the technical field of batteries, in particular to an electric quantity calculation method based on a self-adaptive Gaussian convolution integral method, which comprises the following steps: s1, extracting all charging process data of the vehicle, including SOC; s2, performing Gaussian convolution calculation on the SOC to obtain a capacity estimation value; s3, calculating a mileage value corresponding to the capacity estimation value; s4, optimizing the estimated processing capacity and mileage; and S5, outputting a capacity calculation map. Extracting current data and SOC (state of charge) in each continuous charging process, calculating the SOC by using a self-adaptive Gaussian convolution integral method to obtain a capacity estimation value, then calculating a corresponding mileage value, and finally outputting a capacity calculation graph; the method and the device realize more accurate calculation of the capacity based on the online data, and effectively solve the technical problem that the battery capacity is difficult to accurately estimate through the online data in the prior art.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to an electric quantity calculation method based on a self-adaptive Gaussian convolution integral method.
Background
At present, new energy automobiles are in a rapid development stage, and lithium ion batteries are used as core components, so that the service life of the lithium ion batteries is increasingly concerned by people. The related data show that the power battery for the new energy automobile can not be used on the electric automobile after the battery capacity is reduced to 80% of the rated capacity. As can be seen, in order to be able to immediately disable power when the battery capacity drops to 80%, it is necessary to predict the service life of the lithium ion battery.
In this regard, document CN104101837A discloses an online calculation method for the current total capacity of a battery, which includes the following steps: detecting whether the charging of the battery is finished; acquiring first total charging electric quantity and first total discharging electric quantity of the battery when charging is finished; when the nuclear power state of the battery in the use process is reduced to a first preset value, acquiring second total charging electric quantity and second total discharging electric quantity of the battery; obtaining the current discharging electric quantity according to the first total charging electric quantity, the first total discharging electric quantity, the second total charging electric quantity and the second total discharging electric quantity; obtaining the residual capacity of the battery according to a first preset value; and correcting the nominal capacity of the battery according to the current discharging electric quantity and the residual capacity.
At present, the related technology can realize the online intelligent calculation of the total capacity of the battery, solve the problem of the identification of the total capacity of the battery, save a large amount of manpower and material resources, and simultaneously provide reliable parameter guarantee for the inaccuracy of the SOC of the battery caused by the attenuation of the capacity of the battery.
On a real vehicle, the vehicle-mounted hardware can only collect data such as current, voltage, temperature and the like of the battery, the situation of full charge and full discharge hardly exists on the real vehicle, and the current maximum available capacity of the battery cannot be directly measured. Meanwhile, the SOC (i.e., the state of charge, which reflects the remaining capacity of the battery by percentage, and is numerically defined as the ratio of the remaining capacity to the battery capacity) of the battery is calculated by a BMS (battery management system) rather than being directly measured, and thus a certain error may occur. Therefore, it is difficult for the prior art to accurately estimate the battery capacity through online data, which makes it difficult to effectively estimate the SOH (i.e., the battery health, which is the percentage of the current capacity of the battery to the factory capacity) and predict the life of the current battery.
Disclosure of Invention
The invention provides an electric quantity calculation method based on a self-adaptive Gaussian convolution integral method, which solves the technical problem that the battery capacity is difficult to accurately estimate through online data in the prior art.
The basic scheme provided by the invention is as follows: an electric quantity calculation method based on a self-adaptive Gaussian convolution integral method comprises the following steps:
s1, extracting all charging process data of the vehicle, including SOC;
s2, performing Gaussian convolution calculation on the SOC to obtain a capacity estimation value;
s3, calculating a mileage value corresponding to the capacity estimation value;
s4, optimizing the estimated processing capacity and mileage;
and S5, outputting a capacity calculation map.
The working principle and the advantages of the invention are as follows: extracting current data and SOC in each continuous charging process, calculating the SOC by using a self-adaptive Gaussian convolution integral method to obtain a capacity estimation value, then calculating a corresponding mileage value, and finally outputting a capacity calculation graph. The method realizes more accurate calculation of the capacity based on the online data, and is simple and reliable.
The invention effectively solves the technical problem that the battery capacity is difficult to accurately estimate through online data in the prior art.
Further, in S1, preprocessing the data is further included.
Has the advantages that: the data is preprocessed, so that some wrong data can be eliminated, and the possibility of subsequent errors is reduced.
Further, in S1, the method further includes cutting the data.
Has the advantages that: therefore, the data of each continuous charging process can be clearly distinguished, and the data is prevented from being disordered, so that the analysis result is influenced.
Further, in S2, a capacity estimation value is obtained by the least square method.
Has the advantages that: the least squares method, also known as the least squares method, finds the best functional match of the data by minimizing the sum of the squares of the errors. In this way, unknown capacity estimates can be easily obtained, and the sum of squares of the error between the capacity estimates and the actual data is minimized.
Further, in S2, gaussian filtering is performed on the data.
Has the advantages that: most of data noise belongs to Gaussian noise, and Gaussian filtering is linear smooth filtering, is suitable for eliminating Gaussian noise and is convenient to realize.
Further, in S3, a mileage value corresponding to the capacity estimation value is calculated by using a median algorithm.
Has the advantages that: the median is the number in the middle of a set of data arranged in sequence, and represents a value in a sample, population or probability distribution, and can divide the value set into two equal parts, and is not influenced by the maximum or minimum value of the distribution, so that the mileage value calculated in the way is more representative.
Further, in S4, the method further includes calculating a variance, and excluding the capacity estimation value other than the variance based on a preset confidence interval.
Has the advantages that: the variance is a measure for measuring the discrete degree of data, and the capacity estimation value except the variance is eliminated through a preset confidence interval, so that the data with larger fluctuation can be eliminated.
Further, in S4, the preset confidence interval is 95%.
Has the advantages that: practical experience shows that data beyond the confidence interval of 95% are not very reliable, and the limitation is carried out in such a way that the data are simple, reliable and accurate.
Further, in S4, the variance calculation is looped twice.
Has the advantages that: therefore, accidental errors in a single calculation process can be prevented from influencing subsequent analysis, and the calculation twice is favorable for finding errors in time.
Further, in S5, a capacity calculation map is output by performing third-order polynomial fitting on the optimized capacity estimation value and the mileage.
Has the advantages that: the method adopts third-order polynomial fitting, is simple and easy to implement, and has a good convergence effect.
Drawings
Fig. 1 is a flowchart of an embodiment of a power calculation method based on an adaptive gaussian convolution integral method according to the present invention.
Detailed Description
The following is further detailed by the specific embodiments:
example 1
The embodiment of the electric quantity calculating method based on the self-adaptive Gaussian convolution integral method is basically shown as the attached figure 1, and comprises the following steps:
s1, extracting all charging process data of the vehicle, including SOC;
s2, performing Gaussian convolution calculation on the SOC to obtain a capacity estimation value;
s3, calculating a mileage value corresponding to the capacity estimation value;
s4, optimizing the estimated processing capacity and mileage;
and S5, outputting a capacity calculation map.
On a real vehicle, the vehicle-mounted hardware can only acquire data such as current, voltage, temperature and the like of the battery, meanwhile, full charge and discharge hardly exist on the real vehicle, and the current maximum available capacity of the battery cannot be directly measured. Meanwhile, the SOC of the battery is calculated by the BMS instead of being measured directly, and a certain error exists. Therefore, how to accurately estimate the battery capacity through online data becomes a difficult point and a key point of current cloud battery SOH estimation and service life prediction, and the scheme can estimate the capacity by using historical data of an online vehicle.
And S1, extracting all charging process data of the vehicle.
The data of the charging process comprises SOC, and the data are preprocessed in order to eliminate certain wrong data and reduce the possibility of subsequent errors. For example, data with too large a deviation in value is removed. In addition, in order to clearly distinguish data of each continuous charging process and prevent the data from being confused to influence the analysis result, the data is required to be cut, and the method can be carried out by referring to the prior art.
And S2, performing Gaussian convolution calculation on the SOC to obtain a capacity estimation value.
In order to improve the accuracy of calculation, data are filtered in a Gaussian filtering mode, then Gaussian convolution calculation is carried out on the SOC, and a capacity estimation value is obtained through a least square method.
And S3, calculating the mileage value corresponding to the capacity estimation value.
In order to avoid being affected by the maximum or minimum values of the distribution and make the calculated mileage value more representative, the mileage value corresponding to the capacity estimation value is calculated by using a median algorithm in the embodiment.
And S4, optimizing the estimated processing capacity and the mileage.
The variance is calculated first, and then the capacity estimation value except the variance is excluded based on a preset confidence interval, which is 95% in the embodiment. In order to prevent accidental errors from occurring in a single calculation process, which would affect the subsequent analysis, the calculation of the variance is included twice.
And S5, outputting a capacity calculation map.
And the capacity calculation chart is output by performing third-order polynomial fitting on the optimized capacity estimation value and the optimized mileage number, and the third-order polynomial fitting is simple and easy to implement and has a good convergence effect.
Example 2
The only difference from example 1 is that the determination of whether or not abnormality has occurred in the battery cell is also assisted by the surface temperature of the battery cell. The data of each battery monomer uploaded to the enterprise platform by the new energy automobile comprise temperature data, the temperature data are collected through a temperature sensor, and a probe or a probe of the temperature sensor is in contact with the battery monomer to measure the surface temperature data of the battery monomer in real time.
In this embodiment, each battery cell has a preset number, the numbers correspond to position information of the battery cell installation, and the position information is specifically a horizontal distance and a vertical distance; the horizontal distance refers to a linear distance between the single battery and the cockpit, namely a distance between a geometric center of the cockpit and a geometric center of the single battery; the vertical distance is a straight line distance between the single battery and the ground, and a vertical line is drawn from the geometric center of the single battery to the ground to obtain a vertical point, namely a distance between the geometric center of the single battery and the vertical point. For example, for the battery cell No. 5, the position information thereof may be expressed in a format of "No. 5, horizontal distance-1.2 m, vertical distance-0.20 m", which indicates that the battery cell No. 5 is located at a straight distance of 1.2m from the cockpit and a vertical distance of 0.20m from the ground.
When a certain battery cell needs to be judged whether to be abnormal at a certain moment: step one, determining a temperature threshold corresponding to the moment; secondly, extracting the temperature value at the moment acquired by the temperature sensor; thirdly, correcting the temperature value of the battery monomer at the moment according to the position information of the battery monomer to obtain a corrected temperature value at the moment; fourthly, judging whether the single battery is abnormal or not according to the temperature value corrected at the moment and the temperature threshold value corresponding to the moment: if the temperature value corrected at the moment is larger than or equal to the temperature threshold value corresponding to the moment, judging that the single battery is abnormal; and if the temperature value corrected at the moment is smaller than the temperature threshold corresponding to the moment, judging that the single battery is normal.
Specifically, taking the battery cell No. 5 as an example, it is determined whether or not the battery cell is abnormal at the 50 th second:
first, a temperature threshold corresponding to the 50 th second of the battery cell is determined, and the temperature threshold can be manually preset according to a service life rule of the battery cell, for example, 50 ℃.
In the second step, the temperature value of the battery cell acquired by the temperature sensor in the 50 th second is extracted, for example, 45 ℃.
And thirdly, correcting the temperature value of the battery monomer at the moment according to the position information of the battery monomer to obtain the corrected temperature value at the moment. The position information of the battery cell is ' number-5 ', horizontal distance-1.2 m and vertical distance-0.20 m ', that is, the linear distance between the battery cell and the cockpit is 1.2m, and the linear distance between the battery cell and the ground is 0.20 m. The closer the battery monomer is to the cockpit, the lower the temperature measured by the temperature sensor is than the actual temperature due to the refrigeration of the air conditioner; the closer the battery cell is to the ground, the higher the temperature measured by the temperature sensor is compared with the actual temperature due to the hot air on the ground in summer.
In this embodiment, the reference horizontal distance and the reference vertical distance are set, and the specific correction method is as follows:
horizontal correction: if the horizontal distance is less than or equal to the reference horizontal distance, the corrected temperature is equal to the temperature collected by the temperature sensor plus 0.01 multiplied by the horizontal distance; if the horizontal distance is larger than the reference horizontal distance, the influence of air conditioner refrigeration is small, and correction is not needed.
And (3) vertical correction: if the vertical distance is smaller than or equal to the reference vertical distance, the corrected temperature is equal to the temperature acquired by the temperature sensor, namely the temperature is-0.02 multiplied by the vertical distance; if the vertical distance is larger than the reference vertical distance, the influence of the ground hot air is small, and correction is not needed.
In summary, the corrected temperature is the temperature collected by the temperature sensor +0.01 × horizontal distance — 0.02 × vertical distance. If the reference horizontal distance is 1.4m and the reference vertical distance is 0.3m, the corrected temperature is equal to the temperature acquired by the temperature sensor +0.01 × horizontal distance-0.02 × vertical distance-45 ℃, + +0.01 ℃/cm × 140 cm-0.02 ℃/cm × 20 cm-45 + 1.4-0.4 ═ 46 ℃.
Fourthly, judging whether the single battery is abnormal or not according to the temperature value corrected at the moment and the temperature threshold value corresponding to the moment: the temperature value corrected at the moment is 46 ℃ and is smaller than the temperature threshold value 50 ℃ corresponding to the moment, and the battery cell is judged to be normal.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (9)
1. An electric quantity calculation method based on a self-adaptive Gaussian convolution integral method is characterized by comprising the following steps:
s1, extracting all charging process data of the vehicle, including SOC;
s2, performing Gaussian convolution calculation on the SOC, and obtaining a capacity estimation value by using a least square method;
s3, calculating a mileage value corresponding to the capacity estimation value;
s4, optimizing the estimated processing capacity value and the mileage value;
s5, outputting a capacity calculation chart;
s6, judging whether the battery cell is abnormal at a certain moment through the surface temperature of the battery cell:
s61, determining a temperature threshold corresponding to the moment;
s62, extracting the temperature value at the moment acquired by the temperature sensor;
s63, correcting the temperature value of the battery monomer at the moment according to the position information of the battery monomer to obtain the corrected temperature value at the moment; wherein, the position information is a horizontal distance and a vertical distance; the horizontal distance is the linear distance between the single battery and the cockpit, namely the distance between the geometric center of the cockpit and the geometric center of the single battery; the vertical distance is a straight line distance between the single battery and the ground, and a vertical line is drawn from the geometric center of the single battery to the ground to obtain a vertical point, namely the distance between the geometric center of the single battery and the vertical point;
and S64, judging whether the single battery is abnormal according to the temperature value corrected at the moment and the temperature threshold value corresponding to the moment: if the temperature value corrected at the moment is larger than or equal to the temperature threshold value corresponding to the moment, judging that the single battery is abnormal; and if the temperature value corrected at the moment is smaller than the temperature threshold corresponding to the moment, judging that the single battery is normal.
2. The method of claim 1, wherein the step S1 further comprises preprocessing the data.
3. The method of claim 2, wherein the step S1 further comprises slicing the data.
4. The method of claim 3, wherein the step S2 further comprises Gaussian filtering the data.
5. The method of claim 4, wherein in step S3, a mileage value corresponding to the capacity estimation value is calculated by using a median algorithm.
6. The method of claim 5, wherein the step S4 further comprises calculating a variance, and excluding the capacity estimation value from the variance based on a preset confidence interval.
7. The method of claim 6, wherein the predetermined confidence interval in S4 is 95%.
8. The method of claim 7, wherein in S4, the variance calculation is cycled twice.
9. The method of claim 8, wherein in step S5, a capacity calculation map is output by performing a third-order polynomial fit on the optimized capacity estimation value and mileage value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010761027.8A CN111751732B (en) | 2020-07-31 | 2020-07-31 | Electric quantity calculation method based on self-adaptive Gaussian convolution integral method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010761027.8A CN111751732B (en) | 2020-07-31 | 2020-07-31 | Electric quantity calculation method based on self-adaptive Gaussian convolution integral method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111751732A CN111751732A (en) | 2020-10-09 |
| CN111751732B true CN111751732B (en) | 2021-09-28 |
Family
ID=72712872
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010761027.8A Active CN111751732B (en) | 2020-07-31 | 2020-07-31 | Electric quantity calculation method based on self-adaptive Gaussian convolution integral method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111751732B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117076823A (en) * | 2023-09-25 | 2023-11-17 | 国网四川省电力公司营销服务中心 | Ripple component analysis method, system, equipment and medium based on Gaussian integration method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101022178A (en) * | 2007-03-09 | 2007-08-22 | 清华大学 | Method for estimating nickel-hydrogen power battery charged state based on standard battery model |
| KR20130110355A (en) * | 2012-03-29 | 2013-10-10 | 에스케이이노베이션 주식회사 | Battery management system and method adjusting soc for battery management system |
| CN105467323A (en) * | 2015-03-19 | 2016-04-06 | 万向A一二三系统有限公司 | Method for detecting the available traveling distance of electric vehicle power battery |
| CN105891724A (en) * | 2016-05-05 | 2016-08-24 | 南京航空航天大学 | On-line estimation method for state of charge of lithium ion battery based on extended single particle model |
| CN108363017A (en) * | 2018-02-26 | 2018-08-03 | 大连理工大学 | A kind of retired lithium battery stabilization capability value scaling method of long-time storage |
| CN109669134A (en) * | 2019-02-27 | 2019-04-23 | 浙江科技学院 | A kind of evaluation method of the SOC based on Kalman filtering method |
| CN111142025A (en) * | 2019-12-26 | 2020-05-12 | 珠海格力电器股份有限公司 | Battery SOC estimation method and device, storage medium and electric vehicle |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003249272A (en) * | 2002-02-22 | 2003-09-05 | Sanyo Electric Co Ltd | Battery pack equipped with temperature sensor |
| KR100971343B1 (en) * | 2007-09-28 | 2010-07-20 | 삼성에스디아이 주식회사 | Battery pack using temperature compensated current measuering device |
| US8363425B2 (en) * | 2009-10-20 | 2013-01-29 | Mann + Hummel Gmbh | Temperature sensor mounting arrangement for a battery frame assembly |
| US20120316810A1 (en) * | 2011-06-08 | 2012-12-13 | GM Global Technology Operations LLC | Battery limit calibration based on battery life and performance optimization |
| CN106785216A (en) * | 2016-12-30 | 2017-05-31 | 深圳市麦澜创新科技有限公司 | A kind of battery temperature control |
| JP6512230B2 (en) * | 2017-01-26 | 2019-05-15 | トヨタ自動車株式会社 | Battery system and estimation system |
| CN109449518B (en) * | 2018-11-02 | 2021-06-29 | 奇瑞汽车股份有限公司 | Temperature correction method for power battery system |
| CN109357771B (en) * | 2018-12-05 | 2020-11-10 | 江西迪比科股份有限公司 | Self-correcting detection method for battery temperature of electric automobile |
-
2020
- 2020-07-31 CN CN202010761027.8A patent/CN111751732B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101022178A (en) * | 2007-03-09 | 2007-08-22 | 清华大学 | Method for estimating nickel-hydrogen power battery charged state based on standard battery model |
| KR20130110355A (en) * | 2012-03-29 | 2013-10-10 | 에스케이이노베이션 주식회사 | Battery management system and method adjusting soc for battery management system |
| CN105467323A (en) * | 2015-03-19 | 2016-04-06 | 万向A一二三系统有限公司 | Method for detecting the available traveling distance of electric vehicle power battery |
| CN105891724A (en) * | 2016-05-05 | 2016-08-24 | 南京航空航天大学 | On-line estimation method for state of charge of lithium ion battery based on extended single particle model |
| CN108363017A (en) * | 2018-02-26 | 2018-08-03 | 大连理工大学 | A kind of retired lithium battery stabilization capability value scaling method of long-time storage |
| CN109669134A (en) * | 2019-02-27 | 2019-04-23 | 浙江科技学院 | A kind of evaluation method of the SOC based on Kalman filtering method |
| CN111142025A (en) * | 2019-12-26 | 2020-05-12 | 珠海格力电器股份有限公司 | Battery SOC estimation method and device, storage medium and electric vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111751732A (en) | 2020-10-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10209314B2 (en) | Systems and methods for estimation and prediction of battery health and performance | |
| JP5944291B2 (en) | Battery parameter estimation apparatus and method | |
| CN108037460B (en) | Real-time capacity evaluation method for lithium ion batteries produced in batches | |
| CN111812518B (en) | Battery state monitoring method, storage medium and system | |
| CN105301509A (en) | Combined estimation method for lithium ion battery state of charge, state of health and state of function | |
| CN114148216B (en) | Method, system, equipment and storage medium for detecting battery self-discharge rate abnormality | |
| US11575271B2 (en) | SOC and SOH estimation methods of battery pack | |
| CN111965559A (en) | Lithium ion battery SOH online estimation method | |
| CN110045291B (en) | Lithium battery capacity estimation method | |
| CN110275118B (en) | Power type power battery state of health estimation method | |
| EP3842816A1 (en) | Soh estimation method of battery pack | |
| CN115257457A (en) | Vehicle battery monomer consistency evaluation method, device, equipment and storage medium | |
| CN111751732B (en) | Electric quantity calculation method based on self-adaptive Gaussian convolution integral method | |
| CN113540589B (en) | Battery temperature difference self-adaptive threshold value determination method and system | |
| CN114879070A (en) | Battery state evaluation method and related equipment | |
| CN115219918A (en) | Lithium ion battery life prediction method based on capacity decline combined model | |
| CN114631032A (en) | Method and system for monitoring health of battery pack | |
| CN115327380A (en) | Battery failure diagnosis method, storage medium, and electronic device | |
| CN112946480A (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
| CN116736166A (en) | Battery cell abnormality detection method and device of battery pack and battery pack | |
| CN106815406B (en) | Power battery SOC estimation method based on feature model | |
| CN116893366A (en) | Method and device for operating a system to detect anomalies in an electrical energy store of a device | |
| CN113777497B (en) | Online SOC and SOH joint estimation method and device for degraded battery, storage medium and electronic equipment | |
| CN116256651A (en) | Battery thermal incapacitation early warning method, system and readable storage medium | |
| CN116381498A (en) | Method and apparatus for learning parameterization of aging models and providing state of health of device battery based on open circuit voltage characteristics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |